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International Conference on control, Automation and systems, ICCAS 2007

作者： Ruili, Dong Yonghong, Tan A0503221 Department of Automation Shanghai Jiaotong University Shanghai 200040 China Lab. of Intelligent Syst. and Control Eng. Guilin University of Electronic Technology 54104 Guilin China

ISBN: (纸本)8995003871

An on-line modeling method of the sandwich systems with dead zone is proposed in this paper. In this modeling scheme, the so-called key term separation principle is employed in order to handle the nonsmooth nonlinear part in the system. Then, a Recursive General Identification Algorithm (RGIA) is applied to the estimation of the parameters of the model to describe the characteristic of the sandwich system. In this paper, the convergence of the RGIA will be discussed. Finally, a simulation example based on the proposed method is presented. © ICROS.

关键词： Dead zones

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Modeling Hysteresis Inverse in Piezoelectric Actuators Based on Inverse Hysteretic Operator

Modeling Hysteresis Inverse in Piezoelectric Actuators Based...

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IEEE International Conference on Networking, Sensing and control

作者： Yonghong Tan Laboratory of Intelligent Systems and Control Engineering Guilin University of Electronic and Technology Guilin China

In order to improve the performance of the system with piezoelectric actuators, one of the approaches is to construct an inverse model of the hysteresis to cascade with the actuator so as to compensate for the effect of hysteresis involved in the piezoelectric actuators. In this paper, a neural networks based inverse model for the hysteresis is proposed. In this scheme, an inverse hysteretic operator is proposed to transform the multi-valued mapping of the inverse hysteresis into a kind of one-to-one mapping so that the neural networks are capable of implementing identification for the hysteresis inverse.

关键词： Hysteresis Inverse problems Piezoelectric actuators Neural networks Continuous time systems Data mining Feature extraction

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Construction of Expanded Input Space for Modeling Hysteretic systems

Construction of Expanded Input Space for Modeling Hysteretic...

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IEEE International Symposium on intelligent control (ISIC)

作者： Y. Tan X. Zhao Laboratory of Intelligent Systems and Control Engineering Guilin University of Electronic and Technology Guilin China Institute of Automation Zhejiang Science and Technology University China

A neural model for hysteresis based on expanded input space is proposed in this article. In this method, the behavior of hysteresis is considered as a dynamic system that can be described by a nonlinear state space equation containing hysteretic state. In order to transfer the multi-valued mapping of hysteresis into a one-to-one mapping, an expanded input space involving the original input variable and a so-called Duhem operator is constructed. Thus, the neural networks can be employed to approximate the relation between the hysteretic state and the output of the system that is also the output of hysteresis. The proposed model has a simple architecture that can be easily implemented for on-line adaptation for the model in case of the unexpected change of operating environment. Furthermore, the dynamic performance of the model is improved because of the existence of Duhem operator. Finally, the method is used to the modeling of hysteresis in a piezoelectric actuator.

关键词： Hysteresis Nonlinear equations intelligent actuators control system synthesis Nonlinear dynamical systems Neural networks intelligent control Real time systems Nonlinear systems State-space methods

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Neural nets bBased modeling of inverse model for hysteresis using continuous transformation

Neural nets bBased modeling of inverse model for hysteresis ...

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2006 IEEE International Symposium on intelligent control, ISIC 2006

作者： Lianwei, Ma Yonghong, Tan Department of automatic control Shanghai Jiaotong University Shanghai 200030 China Lab. of Intelligent Systems and Control Engineering Guilin University of Electronic Technology 541004 Guilin China

ISBN: (纸本)0780397983

This paper proposes a novel and simple approach for modeling of the inverse hysteresis. In this method, the continuous transformation technique is utilized to construct an elementary inverse hysteresis model (EIHM), which sets up a one-to-one mapping between the input space and the output space of the inverse hysteresis nonlinearity. Then the output of the EIHM is used as one of the input signals of the neural network (NN) to approximate the inverse behavior of hysteresis. Finally, the proposed method is applied to the modeling of the inverse model of hysteresis inherent in piezoelectric actuator. © 2006 IEEE.

关键词： Hysteresis

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Nonlinear internal model control for hysteresis systems

Nonlinear internal model control for hysteresis systems

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2006 IEEE International Conference on Networking, Sensing and control, ICNSC'06

作者： Yonghong, Tan Miyong, Su Laboratory of Intelligent Systems and Control Engineering Guilin University of Electronic Technology Guilin 541004 China Xidian University China Department of Computer Science Guilin University of Electronic Technology Guilin 541004 China

ISBN: (纸本)1424400651

A hybrid model used to approximate hysteresis and creep nonlinear system is proposed. This model is constructed by a weighted superposition of both backlash operators and creep operators. Then, the corresponding inverse models for both hysteresis and creep are developed. Based on the obtained models, a nonlinear internal model control strategy is applied to the control of system with hysteresis and creep nonlinearities. © 2006 IEEE.

关键词： Nonlinear control systems

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Adaptive control of system with hysteresis using neural networks

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Journal of systems engineering and electronics 2006年第1期17卷 163-167页

作者： Li Chuntao Tan Yonghong Coll. of Automation Nanjing Univ. Aeronautics and Astronautics Nanjing 210016 P. R. China Lab of Intelligent Systems and Control Engineering Guilin Univ. of Electronic Technology Guilin 541000 P. R. China

An adaptive control scheme is developed for a class of single-input nonlinear systems preceded by unknown hysteresis, which is a non-differentiable and multi-value mapping nonlinearity. The controller based on the three-layer neural network （NN）, whose weights are derived from Lyapunov stability analysis, guarantees closed-loop semiglobal stability and convergence of the tracking errors to a small residual set. An example is used to confirm the effectiveness of the proposed control scheme.

关键词： neural networks hysteresis adaptive control preisach model.

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EHM based neural model for hysteresis

EHM based neural model for hysteresis

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2006 IEEE International Conference on Networking, Sensing and control, ICNSC'06

作者： Lianwei, Ma Yonghong, Tan Laboratory of Intelligent Systems and Control Engineering Guilin University of Electronic Technology Guilin 541004 China Department of Automation Shanghai Jiaotong University Shanghai 200030 China

ISBN: (纸本)1424400651

This paper proposes a neural model for hysteresis. A method of continuous transformation is used to construct an elementary hysteresis model (EHM), which implements a one-to-one mapping between the input space and the output space of the hysteresis. The output of the EHM is used as one of the input signals of the neural network (NN) to approximate the behavior of hysteresis. A parabolic factor is introduced to improve the modeling performance for the major and minor loops of the hysteresis. © 2006 IEEE.

关键词： Neural networks

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Nonlinear Internal Model control for Hysteresis systems

Nonlinear Internal Model Control for Hysteresis Systems

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IEEE International Conference on Networking, Sensing and control

作者： Yonghong Tan Miyong Su Laboratory of Intelligent Systems and Control Engineering Guilin University of Electronic and Technology Guilin China Department of Computer Science Guilin University of Electronic and Technology Guilin China Xidian University China

关键词： control system synthesis Nonlinear control systems Magnetic hysteresis Creep Piezoelectric actuators Neural networks Inverse problems control nonlinearities Feedforward neural networks Nonlinear systems

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Neural adaptive control of dynamic sandwich systems with hysteresis

Neural adaptive control of dynamic sandwich systems with hys...

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IEEE International Conference on Computer-Aided Design

作者： Xinlong Zhao Yonghong Tan The Department of automatic control Shanghai Jiaotong University Shanghai China The Laboratory of Intelligent Systems and Control Engineering Guilin University of Electronic and Technology Guilin China

An adaptive control strategy is presented for dynamic sandwich systems with hysteresis. The so-called sandwich system with hysteresis is the system that can be represented by a cascade of a dynamic smooth nonlinear (DSNL1), a hysteresis nonlinear (HNL) and another dynamic smooth nonlinear (DSNL2) subsystem. In this control strategy, a neural network based inverse model is constructed to compensate for the effect of the first dynamic block (i.e. DSNL1) of the sandwich system. Thus, the sandwich system can be transformed into a dynamic nonlinear subsystem preceded by hysteresis. Then a novel hysteretic operator is proposed to transform the multi-valued mapping of hysteresis into a one-to-one mapping. Base on the proposed hysteretic operator, a neural adaptive controller is developed for the modified system. One of the advantages of the controller is that it does not need to construct the inverse model of hysteresis to cancel the hysteretic effect

关键词： Adaptive control Hysteresis Nonlinear dynamical systems control systems Inverse problems Programmable control Neural networks Optimal control Feedback control intelligent control

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Neural nets based modeling of inverse model for hysteresis using continuous transformation

Neural nets based modeling of inverse model for hysteresis u...

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IEEE International Conference on Computer-Aided Design

作者： Lianwei Ma Yonghong Tan Department of automatic control Shanghai Jiaotong University Shanghai China Laboratory of Intelligent Systems and Control Engineering Guilin University of Electronic and Technology Guilin China

关键词： Inverse problems Neural networks Magnetic hysteresis Piezoelectric actuators intelligent control control system synthesis Artificial neural networks Oscillators Piezoelectric materials Magnetic levitation

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